Calendar

50 studenti (classi di prima, seconda e terza media).

Ref. Giovanni Di Salvo (Cell. 3280321831)

scuola Borgese- XVII Maggio di Palermo

visita presso l’Osservatorio

trattare l’argomento dei cambiamenti climatici mediante la visione e spiegazione degli strumenti di raccolta dati meteorologici.

Esperti: Francesco Damiani e Cosimo Rubino

D. Gulli, N. Montinaro, E. Puccio, M. Todaro, U. Lo Cicero, A. Collura

Il telerilevamento tramite pallone consiste nel monitoraggio aereo, tramite l’osservazione a quote comprese tra 100 m e 1000 m e per lunghi periodi di tempo, di vaste aree, difficilmente ottenibile con altre tecnologie similari (droni, aerei). Il pallone aerostatico frenato che si intende utilizzare, poco conosciuto in Italia, è costituito da una bolla contenente elio e da un profilo alare con funzione di sollevamento a portanza. La tecnologia da installare a bordo comprende una videocamera ad alta risoluzione con zoom ottico ad elevato ingrandimento, una termocamera IR e una camera NIR. Le principali applicazioni, in ambito cittadino, possono essere: videosorveglianza per la sicurezza pubblica, monitoraggio dell’abusivismo edilizio, ambientale (discariche abusive, traffico, smog) e di incendi. Ulteriori applicazioni per osservazioni terrestri comprendono il monitoraggio di rifiuti a base di plastica dispersi in mare, di terreni agricoli e loro colture, di animali al pascolo e metereologiche.

40 studenti di fisica

Speaker: I. Pillitteri (INAF-OAPA)

Titolo: MHD numerical simulations of Star-Planet Interaction related  phenomena

Abstract:

The discovery of close-in planets (orbiting within 0.1 AU) since the beginning of the exoplanetary astronomy arises the question of
which phenomena could be related to Star-Planet interaction (SPI). On a theoretical basis SPI is expected to act through tidal and magnetic interaction. In the talk I will review the cases of claimed SPI, the analytical and numerical MHD modeling of SPI and the development of numerical modeling at OAPA

Titolo: Use of supervised machine learning for determination of redshifts of  Gamma-Ray Bursts

Abstract:

Gamma-ray bursts (GRBs) by virtue of their high luminosities are observed up to redshift z=9.4 (Cucchiara et al. 2011), far beyond the most distant quasars or galaxies. and thus have the potential to be vital cosmological probes of earlier processes in the universe, such as reionization, evolution of the star formation rate (SFR), in general, and formation of the first generation (Population III) stars. This requires a relatively large sample of GRBs with known redshifts and well defined observational selection effects. Most GRB instruments provide samples with a well defined prompt gamma-ray peak flux threshold. However, samples with redshift, requiring localization at X-rays and optical-UV follow up observations, suffer from more complex truncations,which hampers the progress to this end. The Swift satellite, the most successful instruments for measuring spectroscopic redshifts of GRBs, has provided redshifts only about one-third of GRBs itdetected. The situation is even less promising for other instruments. Thus, for more than 20 years there have been attempts to increase the number of GRBs with known z via a theoretical estimate of redshift (so called pseudo-redshifts) using GRB relations, but these approaches have led to inaccurate predictions. Thus, we adopt here supervised machine learning approaches to estimate redshifts for GRBs using existing data from the Swift-(satellite). These methods will also allow us to estimate possible non-linear relations between the redshift and other GRB characteristics. Our approach brings a novelty on this research area,because, for the first time, it adds the afterglow plateau emission characteristics. We obtained best results using the “superlearner” ensamble model with a correlation coefficient of 0.86 between the predicted and the observed redshift. We also show that using the predicted redshifts we obtain distributions and cosmological evolutions very similar to those obtained from actual measured redshifts.